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©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 28, 2024; 30(40): 4354-4366
Published online Oct 28, 2024. doi: 10.3748/wjg.v30.i40.4354
Published online Oct 28, 2024. doi: 10.3748/wjg.v30.i40.4354
Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors
Hong-Wei Li, Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
Zi-Yu Zhu, Ying-Wei Xue, Department of Gastroenterological Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
Yu-Fei Sun, Department of Anesthesia, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
Chao-Yu Yuan, Mo-Han Wang, Nan Wang, Department of Computer Science and Technology, Heilongjiang University, Harbin 150000, Heilongjiang Province, China
Co-first authors: Hong-Wei Li and Zi-Yu Zhu.
Co-corresponding authors: Nan Wang and Ying-Wei Xue.
Author contributions: Li HW and Zhu ZY participated in the design of the study and the writing of the manuscript, and they made equal contribution to the manuscript; Sun YF and Yuan CF was involved in data collection and statistical analysis; Wang MH performed the visualization of the research results; Wang N and Xue YW participated in the revision of the manuscript and approved the final manuscript. They made equal contribution to the manuscript. All authors contributed to the article and approved the submitted version.
Supported by the Nn10 Program of Harbin Medical University Cancer Hospital , China, No. Nn10 PY 2017-03.
Institutional review board statement: This study was approved by the Ethics Committee of Harbin Medical University Cancer Hospital (Ethics Approval No. 2019-185) and all participants provided written informed consent. The study design and implementation strictly adhered to the Declaration of Helsinki and relevant ethical guidelines.
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data involved in this study can be obtained from the corresponding author.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ying-Wei Xue, PhD, Professor, Surgical Oncologist, Department of Gastroenterological Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin 150081, Heilongjiang Province, China. xueyingwei@hrbmu.edu.cn
Received: July 8, 2024
Revised: September 19, 2024
Accepted: September 27, 2024
Published online: October 28, 2024
Processing time: 99 Days and 17.1 Hours
Revised: September 19, 2024
Accepted: September 27, 2024
Published online: October 28, 2024
Processing time: 99 Days and 17.1 Hours
Core Tip
Core Tip: This study identified predictive markers and developed machine learning models to assess the prognosis of patients with gastric cancer and treated with immune checkpoint inhibitors. Key findings highlighted the significance of peripheral blood markers such as platelet count/(lymphocyte count × serum prealbumin), prognostic nutrition index, and body mass index in predicting overall survival and progression-free survival. eXtreme Gradient Boosting was the most effective model for prediction and outperformed traditional methods. These insights underscore the potential of machine-learning algorithms in personalized medicine and emphasize the role of nutritional status in treatment outcomes of patients with gastric cancer.